Are You Tampering With My Data?

TL;DR

This paper introduces a data tampering attack that embeds a backdoor into neural network training data, causing misclassification at test time, highlighting vulnerabilities in training data security.

Contribution

The authors present a universal, minimal pixel modification attack on training data that can compromise multiple neural network architectures without model-specific adjustments.

Findings

A single pixel change per class can corrupt training across models

The attack is effective on CIFAR-10 and SVHN datasets

Training data tampering can induce targeted misclassification

Abstract

We propose a novel approach towards adversarial attacks on neural networks (NN), focusing on tampering the data used for training instead of generating attacks on trained models. Our network-agnostic method creates a backdoor during training which can be exploited at test time to force a neural network to exhibit abnormal behaviour. We demonstrate on two widely used datasets (CIFAR-10 and SVHN) that a universal modification of just one pixel per image for all the images of a class in the training set is enough to corrupt the training procedure of several state-of-the-art deep neural networks causing the networks to misclassify any images to which the modification is applied. Our aim is to bring to the attention of the machine learning community, the possibility that even learning-based methods that are personally trained on public datasets can be subject to attacks by a skillful adversary.